Overview

Dataset statistics

Number of variables10
Number of observations214
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)0.5%
Total size in memory16.8 KiB
Average record size in memory80.6 B

Variable types

Numeric10

Alerts

Dataset has 1 (0.5%) duplicate rowsDuplicates
Al is highly overall correlated with Mg and 1 other fieldsHigh correlation
Ba is highly overall correlated with Type of glassHigh correlation
Ca is highly overall correlated with RIHigh correlation
K is highly overall correlated with NaHigh correlation
Mg is highly overall correlated with Al and 1 other fieldsHigh correlation
Na is highly overall correlated with KHigh correlation
RI is highly overall correlated with Ca and 1 other fieldsHigh correlation
Si is highly overall correlated with RIHigh correlation
Type of glass is highly overall correlated with Al and 2 other fieldsHigh correlation
Mg has 42 (19.6%) zerosZeros
K has 30 (14.0%) zerosZeros
Ba has 176 (82.2%) zerosZeros
Fe has 144 (67.3%) zerosZeros

Reproduction

Analysis started2023-12-05 12:32:51.809712
Analysis finished2023-12-05 12:32:58.412771
Duration6.6 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

RI
Real number (ℝ)

HIGH CORRELATION 

Distinct178
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5183654
Minimum1.51115
Maximum1.53393
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-05T20:32:58.469520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.51115
5-th percentile1.515401
Q11.5165225
median1.51768
Q31.5191575
95-th percentile1.523664
Maximum1.53393
Range0.02278
Interquartile range (IQR)0.002635

Descriptive statistics

Standard deviation0.0030368637
Coefficient of variation (CV)0.0020000875
Kurtosis4.9317374
Mean1.5183654
Median Absolute Deviation (MAD)0.001265
Skewness1.6254305
Sum324.9302
Variance9.2225414 × 10-6
MonotonicityNot monotonic
2023-12-05T20:32:58.574407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.52152 3
 
1.4%
1.5159 3
 
1.4%
1.51645 3
 
1.4%
1.51754 2
 
0.9%
1.51841 2
 
0.9%
1.51674 2
 
0.9%
1.51768 2
 
0.9%
1.51655 2
 
0.9%
1.51811 2
 
0.9%
1.52213 2
 
0.9%
Other values (168) 191
89.3%
ValueCountFrequency (%)
1.51115 1
0.5%
1.51131 1
0.5%
1.51215 1
0.5%
1.51299 1
0.5%
1.51316 1
0.5%
1.51321 1
0.5%
1.51409 1
0.5%
1.51508 1
0.5%
1.51514 2
0.9%
1.51531 1
0.5%
ValueCountFrequency (%)
1.53393 1
0.5%
1.53125 1
0.5%
1.52777 1
0.5%
1.52739 1
0.5%
1.52725 1
0.5%
1.52667 1
0.5%
1.52664 1
0.5%
1.52614 1
0.5%
1.52475 1
0.5%
1.5241 1
0.5%

Na
Real number (ℝ)

HIGH CORRELATION 

Distinct142
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.40785
Minimum10.73
Maximum17.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-05T20:32:58.670953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum10.73
5-th percentile12.415
Q112.9075
median13.3
Q313.825
95-th percentile14.8535
Maximum17.38
Range6.65
Interquartile range (IQR)0.9175

Descriptive statistics

Standard deviation0.81660356
Coefficient of variation (CV)0.060904882
Kurtosis3.0522324
Mean13.40785
Median Absolute Deviation (MAD)0.435
Skewness0.45418145
Sum2869.28
Variance0.66684137
MonotonicityNot monotonic
2023-12-05T20:32:58.774201image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.02 5
 
2.3%
13.21 5
 
2.3%
13 5
 
2.3%
13.64 4
 
1.9%
13.33 4
 
1.9%
13.24 4
 
1.9%
12.85 4
 
1.9%
12.86 4
 
1.9%
12.93 3
 
1.4%
13.41 3
 
1.4%
Other values (132) 173
80.8%
ValueCountFrequency (%)
10.73 1
0.5%
11.02 1
0.5%
11.03 1
0.5%
11.23 1
0.5%
11.45 1
0.5%
11.56 1
0.5%
11.95 1
0.5%
12.16 1
0.5%
12.2 1
0.5%
12.3 1
0.5%
ValueCountFrequency (%)
17.38 1
0.5%
15.79 1
0.5%
15.15 1
0.5%
15.01 1
0.5%
14.99 1
0.5%
14.95 2
0.9%
14.94 1
0.5%
14.92 1
0.5%
14.86 2
0.9%
14.85 2
0.9%

Mg
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct94
Distinct (%)43.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6845327
Minimum0
Maximum4.49
Zeros42
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-05T20:32:58.877420image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.115
median3.48
Q33.6
95-th percentile3.85
Maximum4.49
Range4.49
Interquartile range (IQR)1.485

Descriptive statistics

Standard deviation1.4424078
Coefficient of variation (CV)0.53730314
Kurtosis-0.41031896
Mean2.6845327
Median Absolute Deviation (MAD)0.205
Skewness-1.1525593
Sum574.49
Variance2.0805404
MonotonicityNot monotonic
2023-12-05T20:32:58.975871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42
 
19.6%
3.48 8
 
3.7%
3.58 8
 
3.7%
3.54 8
 
3.7%
3.52 7
 
3.3%
3.62 5
 
2.3%
3.57 4
 
1.9%
3.61 4
 
1.9%
3.66 4
 
1.9%
3.5 4
 
1.9%
Other values (84) 120
56.1%
ValueCountFrequency (%)
0 42
19.6%
0.33 1
 
0.5%
0.78 1
 
0.5%
1.01 1
 
0.5%
1.35 1
 
0.5%
1.61 1
 
0.5%
1.71 1
 
0.5%
1.74 1
 
0.5%
1.78 1
 
0.5%
1.83 1
 
0.5%
ValueCountFrequency (%)
4.49 1
 
0.5%
3.98 1
 
0.5%
3.97 1
 
0.5%
3.93 1
 
0.5%
3.9 3
1.4%
3.89 1
 
0.5%
3.87 1
 
0.5%
3.86 1
 
0.5%
3.85 2
0.9%
3.84 1
 
0.5%

Al
Real number (ℝ)

HIGH CORRELATION 

Distinct118
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4449065
Minimum0.29
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-05T20:32:59.077398image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.29
5-th percentile0.696
Q11.19
median1.36
Q31.63
95-th percentile2.394
Maximum3.5
Range3.21
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation0.49926965
Coefficient of variation (CV)0.34553767
Kurtosis2.060569
Mean1.4449065
Median Absolute Deviation (MAD)0.21
Skewness0.90728981
Sum309.21
Variance0.24927018
MonotonicityNot monotonic
2023-12-05T20:32:59.277305image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.54 8
 
3.7%
1.19 6
 
2.8%
1.43 5
 
2.3%
1.29 5
 
2.3%
1.23 5
 
2.3%
1.56 5
 
2.3%
1.36 4
 
1.9%
1.35 4
 
1.9%
1.28 4
 
1.9%
1.25 3
 
1.4%
Other values (108) 165
77.1%
ValueCountFrequency (%)
0.29 1
0.5%
0.34 1
0.5%
0.47 2
0.9%
0.51 1
0.5%
0.56 2
0.9%
0.58 1
0.5%
0.65 1
0.5%
0.66 1
0.5%
0.67 1
0.5%
0.71 1
0.5%
ValueCountFrequency (%)
3.5 1
0.5%
3.04 1
0.5%
3.02 1
0.5%
2.88 1
0.5%
2.79 1
0.5%
2.74 1
0.5%
2.68 1
0.5%
2.66 1
0.5%
2.54 1
0.5%
2.51 1
0.5%

Si
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)62.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.650935
Minimum69.81
Maximum75.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-05T20:32:59.412433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum69.81
5-th percentile71.315
Q172.28
median72.79
Q373.0875
95-th percentile73.5175
Maximum75.41
Range5.6
Interquartile range (IQR)0.8075

Descriptive statistics

Standard deviation0.77454579
Coefficient of variation (CV)0.010661195
Kurtosis2.967903
Mean72.650935
Median Absolute Deviation (MAD)0.385
Skewness-0.73044723
Sum15547.3
Variance0.59992119
MonotonicityNot monotonic
2023-12-05T20:32:59.533807image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.86 4
 
1.9%
72.99 4
 
1.9%
73.1 4
 
1.9%
73.28 4
 
1.9%
73.11 4
 
1.9%
71.99 3
 
1.4%
73.39 3
 
1.4%
72.64 3
 
1.4%
72.95 3
 
1.4%
72.85 3
 
1.4%
Other values (123) 179
83.6%
ValueCountFrequency (%)
69.81 1
0.5%
69.89 1
0.5%
70.16 1
0.5%
70.26 1
0.5%
70.43 1
0.5%
70.48 1
0.5%
70.57 1
0.5%
70.7 1
0.5%
71.15 1
0.5%
71.24 1
0.5%
ValueCountFrequency (%)
75.41 1
0.5%
75.18 1
0.5%
74.55 1
0.5%
74.45 1
0.5%
73.88 1
0.5%
73.81 1
0.5%
73.75 1
0.5%
73.72 1
0.5%
73.7 1
0.5%
73.61 1
0.5%

K
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49705607
Minimum0
Maximum6.21
Zeros30
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-05T20:32:59.636630image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1225
median0.555
Q30.61
95-th percentile0.76
Maximum6.21
Range6.21
Interquartile range (IQR)0.4875

Descriptive statistics

Standard deviation0.65219185
Coefficient of variation (CV)1.3121092
Kurtosis54.689699
Mean0.49705607
Median Absolute Deviation (MAD)0.115
Skewness6.5516483
Sum106.37
Variance0.4253542
MonotonicityNot monotonic
2023-12-05T20:32:59.742082image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30
 
14.0%
0.57 12
 
5.6%
0.56 11
 
5.1%
0.6 11
 
5.1%
0.58 10
 
4.7%
0.64 8
 
3.7%
0.61 8
 
3.7%
0.59 7
 
3.3%
0.55 6
 
2.8%
0.54 6
 
2.8%
Other values (55) 105
49.1%
ValueCountFrequency (%)
0 30
14.0%
0.02 1
 
0.5%
0.03 1
 
0.5%
0.04 2
 
0.9%
0.05 1
 
0.5%
0.06 4
 
1.9%
0.07 1
 
0.5%
0.08 4
 
1.9%
0.09 2
 
0.9%
0.1 1
 
0.5%
ValueCountFrequency (%)
6.21 2
0.9%
2.7 1
0.5%
1.76 1
0.5%
1.68 1
0.5%
1.46 1
0.5%
1.41 1
0.5%
1.1 1
0.5%
0.97 1
0.5%
0.81 1
0.5%
0.76 2
0.9%

Ca
Real number (ℝ)

HIGH CORRELATION 

Distinct143
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9569626
Minimum5.43
Maximum16.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-05T20:32:59.854614image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5.43
5-th percentile7.8125
Q18.24
median8.6
Q39.1725
95-th percentile11.5615
Maximum16.19
Range10.76
Interquartile range (IQR)0.9325

Descriptive statistics

Standard deviation1.4231535
Coefficient of variation (CV)0.15888796
Kurtosis6.681978
Mean8.9569626
Median Absolute Deviation (MAD)0.445
Skewness2.0470539
Sum1916.79
Variance2.0253658
MonotonicityNot monotonic
2023-12-05T20:32:59.959295image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.03 5
 
2.3%
8.43 5
 
2.3%
9.57 4
 
1.9%
8.79 4
 
1.9%
8.44 4
 
1.9%
8.6 3
 
1.4%
8.39 3
 
1.4%
8.55 3
 
1.4%
8.67 3
 
1.4%
9.85 3
 
1.4%
Other values (133) 177
82.7%
ValueCountFrequency (%)
5.43 1
0.5%
5.79 1
0.5%
5.87 1
0.5%
6.47 1
0.5%
6.65 1
0.5%
6.93 1
0.5%
6.96 1
0.5%
7.08 1
0.5%
7.36 1
0.5%
7.59 1
0.5%
ValueCountFrequency (%)
16.19 1
0.5%
14.96 1
0.5%
14.68 1
0.5%
14.4 1
0.5%
13.44 1
0.5%
13.3 1
0.5%
13.24 1
0.5%
12.5 1
0.5%
12.24 1
0.5%
11.64 1
0.5%

Ba
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17504673
Minimum0
Maximum3.15
Zeros176
Zeros (%)82.2%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-05T20:33:00.056076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.57
Maximum3.15
Range3.15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49721926
Coefficient of variation (CV)2.8404944
Kurtosis12.541084
Mean0.17504673
Median Absolute Deviation (MAD)0
Skewness3.4164246
Sum37.46
Variance0.24722699
MonotonicityNot monotonic
2023-12-05T20:33:00.150167image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 176
82.2%
0.64 2
 
0.9%
1.57 2
 
0.9%
0.09 2
 
0.9%
1.59 2
 
0.9%
0.11 2
 
0.9%
3.15 1
 
0.5%
0.81 1
 
0.5%
1.64 1
 
0.5%
1.06 1
 
0.5%
Other values (24) 24
 
11.2%
ValueCountFrequency (%)
0 176
82.2%
0.06 1
 
0.5%
0.09 2
 
0.9%
0.11 2
 
0.9%
0.14 1
 
0.5%
0.15 1
 
0.5%
0.24 1
 
0.5%
0.27 1
 
0.5%
0.4 1
 
0.5%
0.53 1
 
0.5%
ValueCountFrequency (%)
3.15 1
0.5%
2.88 1
0.5%
2.2 1
0.5%
1.71 1
0.5%
1.68 1
0.5%
1.67 1
0.5%
1.64 1
0.5%
1.63 1
0.5%
1.59 2
0.9%
1.57 2
0.9%

Fe
Real number (ℝ)

ZEROS 

Distinct32
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.057009346
Minimum0
Maximum0.51
Zeros144
Zeros (%)67.3%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-05T20:33:00.248924image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.1
95-th percentile0.267
Maximum0.51
Range0.51
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.097438701
Coefficient of variation (CV)1.7091707
Kurtosis2.6620156
Mean0.057009346
Median Absolute Deviation (MAD)0
Skewness1.7543275
Sum12.2
Variance0.0094943004
MonotonicityNot monotonic
2023-12-05T20:33:00.349415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 144
67.3%
0.24 7
 
3.3%
0.17 7
 
3.3%
0.09 6
 
2.8%
0.1 5
 
2.3%
0.11 4
 
1.9%
0.16 3
 
1.4%
0.28 3
 
1.4%
0.12 3
 
1.4%
0.22 3
 
1.4%
Other values (22) 29
 
13.6%
ValueCountFrequency (%)
0 144
67.3%
0.01 1
 
0.5%
0.03 1
 
0.5%
0.05 1
 
0.5%
0.06 1
 
0.5%
0.07 3
 
1.4%
0.08 2
 
0.9%
0.09 6
 
2.8%
0.1 5
 
2.3%
0.11 4
 
1.9%
ValueCountFrequency (%)
0.51 1
 
0.5%
0.37 1
 
0.5%
0.35 1
 
0.5%
0.34 1
 
0.5%
0.32 1
 
0.5%
0.31 1
 
0.5%
0.3 1
 
0.5%
0.29 1
 
0.5%
0.28 3
1.4%
0.26 1
 
0.5%

Type of glass
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7803738
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-05T20:33:00.429808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1037386
Coefficient of variation (CV)0.75663877
Kurtosis-0.2795183
Mean2.7803738
Median Absolute Deviation (MAD)1
Skewness1.1149152
Sum595
Variance4.4257163
MonotonicityIncreasing
2023-12-05T20:33:00.507942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 76
35.5%
1 70
32.7%
7 29
 
13.6%
3 17
 
7.9%
5 13
 
6.1%
6 9
 
4.2%
ValueCountFrequency (%)
1 70
32.7%
2 76
35.5%
3 17
 
7.9%
5 13
 
6.1%
6 9
 
4.2%
7 29
 
13.6%
ValueCountFrequency (%)
7 29
 
13.6%
6 9
 
4.2%
5 13
 
6.1%
3 17
 
7.9%
2 76
35.5%
1 70
32.7%

Interactions

2023-12-05T20:32:57.618978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:51.884468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:52.526660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:53.177931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:53.784378image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:54.436553image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:55.101784image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:55.806185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:56.401881image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:57.017905image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:57.676584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:51.945798image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:52.589724image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:53.236047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:53.844720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:54.501165image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:55.163794image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:55.864280image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:56.461447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:57.075661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:57.739860image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:52.010664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:52.652807image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:53.298289image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:53.911779image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:54.564064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:55.226815image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:55.925144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:56.525250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:57.137413image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:57.802015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:52.071074image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:52.717744image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:53.350255image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:53.970993image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:54.620519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:55.285055image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:55.981559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:56.582645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:57.194406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:57.865692image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:52.136833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:52.782631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:53.410979image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:54.036116image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:54.702484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:55.346847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:56.043147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:56.645524image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:57.255523image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:57.926845image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:52.200334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:52.848625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:53.472853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:54.103907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:54.782660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:55.407993image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:56.101615image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:56.705639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:57.316215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:57.989338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:52.265760image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:52.917709image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:53.534025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:54.172327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:54.846698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:55.468459image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:56.162207image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:56.769169image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:57.376871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:58.048683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:52.328438image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:52.980897image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:53.591289image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:54.232956image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:54.905742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:55.529126image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:56.219668image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:56.831506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:57.437202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:58.110614image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:52.393965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:53.045973image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:53.656609image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:54.295922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:54.970075image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:55.590952image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:56.280800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:56.892263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:57.498653image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:58.172179image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:52.460120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:53.106969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:53.717748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:54.359051image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:55.028345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:55.648931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:56.340583image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:56.951942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:57.556802image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-12-05T20:33:00.577300image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
AlBaCaFeKMgNaRISiType of glass
Al1.0000.475-0.281-0.0760.153-0.5120.136-0.4920.1970.560
Ba0.4751.000-0.0080.010-0.260-0.4560.411-0.1820.1700.507
Ca-0.281-0.0081.0000.112-0.473-0.2890.0270.704-0.2220.053
Fe-0.0760.0100.1121.0000.0920.095-0.2180.096-0.072-0.150
K0.153-0.260-0.4730.0921.0000.201-0.585-0.288-0.001-0.236
Mg-0.512-0.456-0.2890.0950.2011.000-0.1260.144-0.337-0.590
Na0.1360.4110.027-0.218-0.585-0.1261.0000.031-0.2660.399
RI-0.492-0.1820.7040.096-0.2880.1440.0311.000-0.526-0.213
Si0.1970.170-0.222-0.072-0.001-0.337-0.266-0.5261.0000.149
Type of glass0.5600.5070.053-0.150-0.236-0.5900.399-0.2130.1491.000

Missing values

2023-12-05T20:32:58.261104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-05T20:32:58.371182image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RINaMgAlSiKCaBaFeType of glass
01.5210113.644.491.1071.780.068.750.00.001
11.5176113.893.601.3672.730.487.830.00.001
21.5161813.533.551.5472.990.397.780.00.001
31.5176613.213.691.2972.610.578.220.00.001
41.5174213.273.621.2473.080.558.070.00.001
51.5159612.793.611.6272.970.648.070.00.261
61.5174313.303.601.1473.090.588.170.00.001
71.5175613.153.611.0573.240.578.240.00.001
81.5191814.043.581.3772.080.568.300.00.001
91.5175513.003.601.3672.990.578.400.00.111
RINaMgAlSiKCaBaFeType of glass
2041.5161714.950.02.2773.300.008.710.670.07
2051.5173214.950.01.8072.990.008.611.550.07
2061.5164514.940.01.8773.110.008.671.380.07
2071.5183114.390.01.8272.861.416.472.880.07
2081.5164014.370.02.7472.850.009.450.540.07
2091.5162314.140.02.8872.610.089.181.060.07
2101.5168514.920.01.9973.060.008.401.590.07
2111.5206514.360.02.0273.420.008.441.640.07
2121.5165114.380.01.9473.610.008.481.570.07
2131.5171114.230.02.0873.360.008.621.670.07

Duplicate rows

Most frequently occurring

RINaMgAlSiKCaBaFeType of glass# duplicates
01.5221314.213.820.4771.770.119.570.00.012